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Alex Woodie, HPC Wire, The 5 Minute Guide to Parallel and Vector Software Programming, here.
The series will cover a different aspect of HPC programming every week for 12 weeks with the new series called The Five Minute Guide to Parallel and Vector Software Programming. New episodes will come out every Wednesday through the middle of August.
Unknown Lamer, Slashdot, Apple update MacBooks and Mac Pro Desktop with Haswell, “Unified Thermal Core”, here.
“On the hardware side, Apple is updating its two MacBook Air devices; both the 11-inch and 13-inch versions will enjoy better battery life (up to 9 hours and 12 hours, respectively), thanks in no small part to having Intel’s new Haswell processors inside. They’ll also have 802.11ac WiFi on board. Both models have 1.3GHz Intel Core i5 or i7 (Haswell) processors, Intel HD Graphics 5000, 4GB of RAM, and has 128GB or 256GB of flash storage. Arguably the scene stealer on the desktop side of things is a completely redesigned Mac Pro. The 9.9-inch tall cylindrical computer boasts a new ‘unified thermal core’ which is designed to conduct heat away from the CPU and GPU while distributing it uniformly and using a single bottom-mounted intake fan. It rocks a 12-core Intel Xeon processor, dual AMD FirePro GPUs (standard), 1866MHz DDR3 ECC memory (60GBps), and PCIe flash storage with up to 1.25GBps read speeds. The system promises 7 teraflops of graphics performance, supports 4k displays, and has a host of ports including four USB 3.0, two gigabit Ethernet ports, HDMI 1.4, six Thunderbolt 2 ports that offer super-fast (20Gbps) external connectivity.”
Filippo Radicchi, PLOS one, Who Is the Best Player Ever? A complex Network Analysis of the History of Professional Tennis, here. Jim Downs is pleased.
We considered all matches played by professional tennis players between 1968 and2010, and, on the basis of this data set, constructed a directed and weighted network of contacts. The resulting graph showed complex features, typical of many real networked systems studied in literature. We developed a diffusion algorithm and applied it to the tennis contact network in order to rank professional players. Jimmy Connors was identified as the best player in the history of tennis according to our ranking procedure. We performed a complete analysis by determining the best players on specific playing surfaces as well as the best ones in each of the years covered by the data set. The results of our technique were compared to those of two other well established methods. In general, we observed that our ranking method performed better: it had a higher predictive power and did not require the arbitrary introduction of external criteria for the correct assessment of the quality of players. The present work provides novel evidence of the utility of tools and methods of network theory in real applications.
Eric Zivot, coursera, U Washington, Introduction to Computational Finance and Financial Econometrics, here.
Learn mathematical and statistical tools and techniques used in quantitative and computational finance. Use the open source R statistical programming language to analyze financial data, estimate statistical models, and construct optimized portfolios. Analyze real world data and solve real world problems.
Introduction to Computational Finance and Financial Econometrics, Eric Zivot and R. Douglas Martin. Manuscript under preparationStatistics and Data Analysis for Financial Engineering by David Ruppert, Springer-Verlag.Beginner’s Guide to R by Alain Zuur, Elena Ieno and Erik Meesters, Springer-Verlag.R Cookbook by Paul Teetor, O’Reilly.Other books for further reference:Introductory Statistics with R, Second Edition (Statistics and Computing, Paperback), by Peter Dalgaard, Springer-Verlag, New York.Modern Portfolio Theory and Investment Analysis, by E.J. Elton et al., Wiley, New York.Financial Modeling, by Simon Benninga. MIT Press.Statistical Analysis of Financial data in S-PLUS, by Rene Carmona, Springer-Verlag, 2004.
Not Even Wrong, Fall Course: Quantum Mechanics for Mathematicians, here. Check it out. Sry I did not see earlier.
This fall I’m teaching on quantum mechanics for mathematicians, at the undergraduate level. There’s a web-page with more information here. I’ll be writing up lecture notes, which should appear on that web-page as the course goes on, starting Wednesday.
Coursera, website, here. Jeff Ullman is teaching Automata; It’s like Gandalf is coming back to the Shire to teach the Hobbitses. Sry, cannot do this all in LOTR metaphor, perhaps we need to shift metaphor to nail down the dynamics here. These Coursera guys are like the Lakers. Knuth is letting Sedgewick be the Algorithms guy, very Kareem/Bynum. Need Bill Simmons to pick this up at Grantland to do this right.
edX, website, here. These edX guys are the Celtics. Agarwal is fronting this now and he is a Rondoesque stud, but where are the big three? Wait until they finish negotiating with Michael Sandel and Justice maybe we see the big three then. Sandel is like the LBJ of online education (certainly more than a Paul Pierce equivalent). Sandel should be inline for a Lebron James-style payout here. edX better be offering a MAX+ contract there as well as the Harvard economic equivalent of a shoe deal, whatever that might be. Maybe he gets his Chair multiply endowed, or something. Is it recoverable if he decides the right thing to do is not sign with edX? Does Sandel have a posse? How great would that be if Sandel had a press conference to announce “I’m taking my talents to South Beach.” The beantown fans would revolt and burn their Justice jerseys and backpacks; they’ll have to start an edX Cares set of television spots where Mankiw and Stallman talk about how personally gratifying it is to give back to the community.
Udacity, blog, here. The Heat? This could be their year or the only defender that works against these guys is the fourth quarter. These guys better do a deal with Oxford/Cambridge kind of soon. Looks like UCB signed with Coursera.
NYT, Online Education Venture Lures Cash Infusion and Deals With 5 Top Universities, here.
An interactive online learning system created by two Stanford computer scientists plans to announce Wednesday that it has secured $16 million in venture capital and partnerships with four major universities.
The scientists, Andrew Ng and Daphne Koller, taught free Web-based courses through Stanford last year that reached more than 100,000 students. Now they have formed a company, Coursera, as a Web portal to distribute a broad array of interactive courses in the humanities, social sciences, physical sciences and engineering.
Besides Stanford, the university partners include the University of Michigan, the University of Pennsylvania and Princeton.
NYT, Harvard and M.I.T. Team Up to Offer Free Online Courses, here.
In what is shaping up as an academic Battle of the Titans — one that offers vast new learning opportunities for students around the world — Harvard and the Massachusetts Institute of Technology on Wednesday announced a new nonprofit partnership, known as edX, to offer free online courses from both universities.
BBC News magazine, Black-Scholes: The maths formula linked to the financial crash, here.
It’s not every day that someone writes down an equation that ends up changing the world. But it does happen sometimes, and the world doesn’t always change for the better. It has been argued that one formula known as Black-Scholes, along with its descendants, helped to blow up the financial world.
It doesn’t say if Scotland Yard had Scholes in for questioning yet. Oh, this story is sourced from Ian Stewart the math guy from Warwick.
Stewart says the lessons from Long-Term Capital Management were obvious. “It showed the danger of this kind of algorithmically-based trading if you don’t keep an eye on some of the indicators that the more conventional people would use,” he says. “They [Long-Term Capital Management] were committed, pretty much, to just ploughing ahead with the system they had. And it went wrong.”
Scholes says that’s not what happened at all. “It had nothing to do with equations and nothing to do with models,” he says. “I was not running the firm, let me be very clear about that. There was not an ability to withstand the shock that occurred in the market in the summer and fall of late 1998. So it was just a matter of risk-taking. It wasn’t a matter of modelling.”
Would it be a bad thing if John Meriwether and Myron Scholes attend a remedial applied maths course taught by Professor Stewart? Perhaps not, it could be awesome if there is You Tube video of the class.
Craig’s List, I will legally change my name to yours for a WWDC ticket, here. I like how Gruber posts stuff that induces Karl Denninger (here) to call a market top on AAPL, Gruber records it in Claim Chowder on Daring Fireball, and then Gruber spikes the unfortunate Karl Denninger 6 months later. It’s like who killed Kenny in South Park. I am worried however that John Gruber is just an alias for Karl Denninger, which would make the world a smaller, less predictable, and meaner place, so I won’t think about that.
Turing’s Invisible Hand, I grade grad AI, here. Nice slides from the course.
This semester I have been co-teaching (with the awesome Martial Hebert) CMU’sgraduate artificial intelligence (grad AI) course. It’s been a lot of fun teaching AI to a class where a significant fraction of the students build robots for a living (possibly some of the students are robots, especially the ones who got a perfect score on the midterm exam). Although the last class is on May 2, I already gave my last lecture, so this seems like a good time to share some thoughts.
My general impression is that many AI courses try to cover all of AI, broadly defined. Granted, you get Renaissance students who can write “hello world” in Prolog while reciting the advantages and disadvantages of iterative deepening depth-first search. On the down side, breadth comes at the expense of depth, and the students inevitably get the very wrong impression that AI is a shallow field. Another issue is that AI is so broad that some if its subdisciplines are only loosely related, and in particular someone specializing in, say, algorithmic economics, may not be passionate about teaching, say, logic (to give a completely hypothetical example).
Business Insider, BLANKFEIN: The Only Reason Goldman Got Into Trouble Is Because Our Competitors Sucked At Risk Management, here.
DealBreaker, Marvel At The Derivative On Its Derivatives That Credit Suisse Wrote To Itself, here. This looks like the mezz tranche CS awarded for end of year compensation a couple years back. I stopped reading Deal Breaker for a while, but Levine has been very solid recently.
Business Week, Stock Trading Is About to Get 5.2 Milliseconds Faster, here. 59.6 milliseconds NYC to Lon roundtrip latency.
HPC Wire, Intel Makes a Deal for Cray’s Interconnect Technology, here. So Cray wants out of the interconnect hardware business.
Supercomputer maker Cray is methodically and inevitably shifting its technology focus from hardware to software. Another step in that direction played itself out this week in the company’s sale of its highly treasured supercomputing interconnect technology. On Tuesday evening, Cray and Intel announced that they signed a “definitive agreement” that would transfer the interconnect program and expertise to the x86 chipmaker.
Cluster Monkey, Cluster Interconnects, here.
This article will focus on interconnects that aren’t tied to vendor specific node hardware, but can work in a variety of cluster nodes. While determining which interconnect to use is beyond the scope of this article, I can present what is available and make some basic comparisons. I’ll present information that I have obtained from the vendors websites, from information people have posted to the beowulf mailing list, the vendors, and various other places. I won’t make any judgments or conclusions about the various options because, simply, I can’t. The choice of an interconnect depends on your situation and there is no universal solution. I also intend to stay “vendor neutral” but will make observations where appropriate. Finally, I have created a table that presents various performance aspects of the interconnects. There is also a table with list prices for 8 nodes, 24 nodes, and 128 nodes to give you an idea of costs.
Virtually all semiconductor market domains, including PCs, game consoles, mobile handsets, servers, supercomputers, and networks, are converging to concurrent platforms. There are two important reasons for this trend. First, these concurrent processors can potentially offer more effective use of chip space and power than traditional monolithic microprocessors for many demanding applications. Second, an increasing number of applications that traditionally used Application Specific Integrated Circuits (ASICs) are now implemented with concurrent processors in order to improve functionality and reduce engineering cost. The real challenge is to develop applications software that effectively uses these concurrent processors to achieve efficiency and performance goals.
The aim of this course is to provide students with knowledge and hands-on experience in developing applications software for processors with massively parallel computing resources. In general, we refer to a processor as massively parallel if it has the ability to complete more than 64 arithmetic operations per clock cycle. Many commercial offerings from NVIDIA, AMD, and Intel already offer such levels of concurrency. Effectively programming these processors will require in-depth knowledge about parallel programming principles, as well as the parallelism models, communication models, and resource limitations of these processors. The target audiences of the course are students who want to develop exciting applications for these processors, as well as those who want to develop programming tools and future implementations for these processors.
We will be using NVIDIA processors and the CUDA programming tools in the lab section of the course. Many have reported success in performing non-graphics parallel computation as well as traditional graphics rendering computation on these processors. You will go through structured programming assignments before being turned loose on the final project. Each programming assignment will involve successively more sophisticated programming skills. The final project will be of your own design, with the requirement that the project must involve a demanding application such as mathematics- or physics-intensive simulation or other data-intensive computation, followed by some form of visualization and display of results.
Stanford class here.
Virtually all semiconductor market domains, including PCs, game consoles, mobile handsets, servers, supercomputers, and networks, are converging to concurrent platforms. There are two important reasons for this trend. First, these concurrent processors can potentially offer more effective use of chip space and power than traditional monolithic microprocessors for many demanding applications. Second, an increasing number of applications that traditionally used Application Specific Integrated Circuits (ASICs) are now implemented with concurrent processors in order to improve functionality and reduce engineering cost. The real challenge is to develop applications software that effectively uses these concurrent processors to achieve efficiency and performance goals.
The aim of this course is to provide students with knowledge and hands-on experience in developing applications software for processors with massively parallel computing resources. In general, we refer to a processor as massively parallel if it has the ability to complete more than 64 arithmetic operations per clock cycle. Many commercial offerings from NVIDIA, AMD, and Intel already offer such levels of concurrency. Effectively programming these processors will require in-depth knowledge about parallel programming principles, as well as the parallelism models, communication models, and resource limitations of these processors. The target audiences of the course are students who want to develop exciting applications for these processors, as well as those who want to develop programming tools and future implementations for these processors.
We will be using NVIDIA processors and the CUDA™ programming tools in the lab section of the course. Many have reported success in performing non-graphics parallel computation as well as traditional graphics rendering computation on these processors. You will go through structured programming assignments before being turned loose on the final project. Each programming assignment will involve successively more sophisticated programming skills. The final project will be of your own design, with the requirement that the project must involve a demanding application such as mathematics- or physics-intensive simulation or other data-intensive computation, followed by some form of visualization and display of results.
This is a course in programming massively parallel processors for general computation. We are fortunate to have the support of David Kirk, the Chief Scientist of NVIDIA and one of the main driving forces behind the new NVIDIA CUDA™ technology. Building on architecture knowledge from ECE 411, and general C programming knowledge, we will expose you to the tools and techniques you will need to attack a real-world application for the final project. The final projects will be supported by some real application groups at UIUC and around the country, such as biomedical imaging and physical simulation.
U Illinois class here.
